Suggesting targeting information for ads, such as Websites and/or categories of Websites for example

ABSTRACT

One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information. Each of the taxonomy categories may be a vertical category, and at least one of the set of one or more determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each of the taxonomy categories may have at least one property (e.g., Web document), that participates in an advertising network, associated with it. An advertiser selection of a suggested taxonomy category may be accepted, the serving of an ad of the advertiser may be targeted to each of the at least one property (e.g., Web document) associated with the selected suggested taxonomy category. An offer for association with the selected suggested taxonomy category may be provided by the advertiser. A set of one or more properties (e.g., Web documents) may be determined using at least some of the determined one or more taxonomy categories. Such properties (perhaps along with viewing information) may be presented to an advertising user as an ad targeting suggestion. A suggested property (e.g., Web document) may be selected by a user. If so, the serving of an ad of the advertiser may be targeted to the selected suggested property. An offer for association with the selected suggested document may be accepted from the advertiser. The set of one or more taxonomy categories may be performed by determining a set of one or more semantic clusters (e.g., term co-occurrence clusters) using the accepted keyword(s) and/or property information, and determining a set of one or more taxonomy categories using at least some of the one or more semantic clusters.

§ 1. BACKGROUND OF THE INVENTION

§ 1.1 Field of the Invention

The present invention concerns advertising, such as online advertisingfor example. In particular, the present invention concerns helpingadvertisers to effectively target the presentation of their ads.

§ 1.2 Background Information

Advertising using traditional media, such as television, radio,newspapers and magazines, is well known. Unfortunately, even when armedwith demographic studies and entirely reasonable assumptions about thetypical audience of various media outlets, advertisers recognize thatmuch of their ad budget is simply wasted. Moreover, it is very difficultto identify and eliminate such waste.

Recently, advertising over more interactive media has become popular.For example, as the number of people using the Internet has exploded,advertisers have come to appreciate media and services offered over theInternet as a potentially powerful way to advertise.

Interactive advertising provides opportunities for advertisers to targettheir ads to a receptive audience. That is, targeted ads are more likelyto be useful to end users since the ads may be relevant to a needinferred from some user activity (e.g., relevant to a user's searchquery to a search engine, relevant to content in a document requested bythe user, etc.). Query keyword targeting has been used by search enginesto deliver relevant ads. For example, the AdWords advertising system byGoogle of Mountain View, Calif., delivers ads targeted to keywords fromsearch queries. Similarly, content targeted ad delivery systems havebeen proposed. For example, U.S. patent application Ser. Nos. 10/314,427(incorporated herein by reference and referred to as “the '427application”) titled “METHODS AND APPARATUS FOR SERVING RELEVANTADVERTISEMENTS”, filed on Dec. 6, 2002 and listing Jeffrey A. Dean,Georges R. Harik and Paul Buchheit as inventors; and 10/375,900(incorporated by reference and referred to as “the '900 application”)titled “SERVING ADVERTISEMENTS BASED ON CONTENT,” filed on Feb. 26, 2003and listing Darrell Anderson, Paul Buchheit, Alex Carobus, Claire Cui,Jeffrey A. Dean, Georges R. Harik, Deepak Jindal and NarayananShivakumar as inventors, describe methods and apparatus for serving adsrelevant to the content of a document, such as a Web page for example.Content targeted ad delivery systems, such as the AdSense advertisingsystem by Google for example, have been used to serve ads on Web pages.

An “ad network” is an aggregated set of Websites (and/or some othermedia properties) on which advertisers can place ads by paying a singleparty. Many ad networks organize their Websites by human created andmaintained “verticals” (groups of related products, services,industries, and/or topics that are likely to be found in Websitecontent). For example, “Slashdot.org” is part of the technologyvertical/Computers & Technology and “iVillage” is part of the family andhome vertical/Lifestyle & Communities/Womens Issues. Advertisers pay tohave their ads shown on Websites that are part of these predefinedverticals.

Unfortunately, the predefined verticals often only approximate the realneed advertisers have in reaching their audience since target audiencesmight not fit (e.g., may be more granular than, might not be covered by,etc.) a predefined vertical. For example, an advertiser wanting totarget student software developers might have to target their ad(s) toall Websites in a “technology” vertical to reach this audience. Thus, inad networks that aggregate Websites belonging to a vertical orverticals, the vertical or verticals are often too broad for the needsof many advertisers.

Hierarchically arranged verticals may be used to offer narrower orbroader targeting options. However, a difficult challenge for adnetworks using hierarchical verticals is maintaining the verticalhierarchy. Further, if more Websites are added to a more granularvertical, or if enough advertisers demand a more granular cut of anexisting vertical, then the ad targeting system may want to add a newvertical. However, even if such a new vertical is provided, advertisersmight not use it. For example, the advertisers might not know of the newvertical, or the human work required to get the more granular targetingmight not be worth the effort, etc.

As can be appreciated from the foregoing, present ad networks typicallyuse manually defined vertical “buckets” or “silos” to organize theirnetwork of Websites for ad selection. This approach has manyinefficiencies. For example, most ad networks only represent a set ofWebsites which can be organized by human judgment. These inefficienciesare exacerbated when the advertising network handles more advertisersand/or Websites.

In view of the foregoing problems with existing ad networks, it would beuseful to allow advertisers to define and/or organize a set of Websiteswithin an advertising network to meet their specific marketing needswithout having to rely solely on publisher-defined and inflexibleverticals.

§ 2. SUMMARY OF THE INVENTION

Embodiments consistent with the present invention may (a) accept one ormore keywords, and (b) determine a set of one or more taxonomycategories using at least some of the one or more keywords. Similarly,embodiments consistent with the present invention may (a) acceptinformation about one or more properties (e.g., Web documents), and (b)determine a set of one or more taxonomy categories using at least someof the information of the one or more properties.

In at least some embodiments consistent with the present invention, eachof the taxonomy categories is a vertical category, and at least one ofthe set of one or more determined taxonomy categories may be presentedto an advertising user as an ad targeting suggestion. Each of thetaxonomy categories may have at least one property (e.g., Web document),that participates in an advertising network, associated with it.

In at least some embodiments consistent with the present invention anadvertiser selection of a suggested taxonomy category may be accepted,the serving of an ad of the advertiser may be targeted to each of the atleast one property (e.g., Web document) associated with the selectedsuggested taxonomy category. An offer for association with the selectedsuggested taxonomy category may be provided by the advertiser.

In at least some embodiments consistent with the present invention, aset of one or more properties (e.g., Web documents) are determined usingat least some of the determined one or more taxonomy categories. Suchproperties (perhaps along with viewing information) may be presented toan advertising user as an ad targeting suggestion. A suggested propertymay be selected by a user. If so, the serving of an ad of the advertisermay be targeted to the selected suggested property. An offer forassociation with the selected suggested property may be accepted fromthe advertiser.

In at least some embodiments consistent with the present invention, theact of determining a set of one or more taxonomy categories using atleast some of the keyword(s) and/or property information may beperformed by determining a set of one or more semantic clusters (e.g.,term co-occurrence clusters) using the accepted keyword(s) and/orproperty information, and determining a set of one or more taxonomycategories using at least some of the one or more semantic clusters.

§ 3. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing parties or entities that can interact withan advertising system.

FIG. 2 is a diagram illustrating an environment in which, or with which,embodiments consistent with the present invention may operate.

FIG. 3 is a bubble diagram of exemplary operations that may be performedin a manner consistent with the present invention, as well asinformation that may be used and/or generated by such operations.

FIG. 4 is a flow diagram of an exemplary method for determining categoryand/or document suggestions from input keywords, in a manner consistentwith the present invention.

FIG. 5 is a flow diagram of an exemplary method for determining categoryand/or document suggestions from input documents, in a manner consistentwith the present invention.

FIG. 6 is a flow diagram of an exemplary method for providing anadvertiser user interface, in a manner consistent with the presentinvention.

FIG. 7 is a block diagram of apparatus that may be used to perform atleast some operations, and store at least some information, in a mannerconsistent with the present invention.

FIGS. 8-11 illustrate how an advertiser can target the serving of its adon certain documents, or certain types of documents, using an exemplarymethod consistent with the present invention.

§ 4. DETAILED DESCRIPTION

The present invention may involve novel methods, apparatus, messageformats, and/or data structures for helping advertisers target theserving of an advertisement. The following description is presented toenable one skilled in the art to make and use the invention, and isprovided in the context of particular applications and theirrequirements. Thus, the following description of embodiments consistentwith the present invention provides illustration and description, but isnot intended to be exhaustive or to limit the present invention to theprecise form disclosed. Various modifications to the disclosedembodiments will be apparent to those skilled in the art, and thegeneral principles set forth below may be applied to other embodimentsand applications. For example, although a series of acts may bedescribed with reference to a flow diagram, the order of acts may differin other implementations when the performance of one act is notdependent on the completion of another act. Further, non-dependent actsmay be performed in parallel. No element, act or instruction used in thedescription should be construed as critical or essential to the presentinvention unless explicitly described as such. Also, as used herein, thearticle “a” is intended to include one or more items. Where only oneitem is intended, the term “one” or similar language is used. Thus, thepresent invention is not intended to be limited to the embodiments shownand the inventors regard their invention to include any patentablesubject matter described.

In the following definitions of terms that may be used in thespecification are provided in § 4.1. Then, environments in which, orwith which, the present invention may operate are described in § 4.2.Exemplary embodiments of the present invention are described in § 4.3.Thereafter, a specific example illustrating the usefulness of oneexemplary embodiment of the present invention is provided in § 4.4.Finally, some conclusions regarding the present invention are set forthin § 4.5.

§ 4.1 Definitions

Online ads may have various intrinsic features. Such features may bespecified by an application and/or an advertiser. These features arereferred to as “ad features” below. For example, in the case of a textad, ad features may include a title line, ad text, and an embedded link.In the case of an image ad, ad features may include images, executablecode, and an embedded link. Depending on the type of online ad, adfeatures may include one or more of the following: text, a link, anaudio file, a video file, an image file, executable code, embeddedinformation, etc.

When an online ad is served, one or more parameters may be used todescribe how, when, and/or where the ad was served. These parameters arereferred to as “serving parameters” below. Serving parameters mayinclude, for example, one or more of the following: features of(including information on) a document on which, or with which, the adwas served, a search query or search results associated with the servingof the ad, a user characteristic (e.g., their geographic location, thelanguage used by the user, the type of browser used, previous pageviews, previous behavior, user account, any Web cookies used by thesystem, user device characteristics, etc.), a host or affiliate site(e.g., America Online, Google, Yahoo) that initiated the request, anabsolute position of the ad on the page on which it was served, aposition (spatial or temporal) of the ad relative to other ads served,an absolute size of the ad, a size of the ad relative to other ads, acolor of the ad, a number of other ads served, types of other adsserved, time of day served, time of week served, time of year served,etc. Naturally, there are other serving parameters that may be used inthe context of the invention.

Although serving parameters may be extrinsic to ad features, they may beassociated with an ad as serving conditions or constraints. When used asserving conditions or constraints, such serving parameters are referredto simply as “serving constraints” (or “targeting criteria”). Forexample, in some systems, an advertiser may be able to target theserving of its ad by specifying that it is only to be served onweekdays, no lower than a certain position, only to users in a certainlocation, etc. As another example, in some systems, an advertiser mayspecify that its ad is to be served only if a page or search queryincludes certain keywords or phrases. As yet another example, in somesystems, an advertiser may specify that its ad is to be served only if adocument being served includes certain topics or concepts, or fallsunder a particular cluster or clusters, or some other classification orclassifications. In some systems, an advertiser may specify that its adis to be served only to (or is not to be served to) user devices havingcertain characteristics. Finally, in some systems an ad might betargeted so that it is served in response to a request sourced from aparticular location, or in response to a request concerning a particularlocation.

“Ad information” may include any combination of ad features, ad servingconstraints, information derivable from ad features or ad servingconstraints (referred to as “ad derived information”), and/orinformation related to the ad (referred to as “ad related information”),as well as an extension of such information (e.g., information derivedfrom ad related information).

The ratio of the number of selections (e.g., clickthroughs) of an ad tothe number of impressions of the ad (i.e., the number of times an ad isrendered) is defined as the “selection rate” (or “clickthrough rate”) ofthe ad.

A “conversion” is said to occur when a user consummates a transactionrelated to a previously served ad. What constitutes a conversion mayvary from case to case and can be determined in a variety of ways. Forexample, it may be the case that a conversion occurs when a user clickson an ad, is referred to the advertiser's Web page, and consummates apurchase there before leaving that Web page. Alternatively, a conversionmay be defined as a user being shown an ad, and making a purchase on theadvertiser's Web page within a predetermined time (e.g., seven days). Inyet another alternative, a conversion may be defined by an advertiser tobe any measurable/observable user action such as, for example,downloading a white paper, navigating to at least a given depth of aWebsite, viewing at least a certain number of Web pages, spending atleast a predetermined amount of time on a Website or Web page,registering on a Website, etc. Often, if user actions don't indicate aconsummated purchase, they may indicate a sales lead, although useractions constituting a conversion are not limited to this. Indeed, manyother definitions of what constitutes a conversion are possible.

The ratio of the number of conversions to the number of impressions ofthe ad (i.e., the number of times an ad is rendered) is referred to asthe “conversion rate.” If a conversion is defined to be able to occurwithin a predetermined time since the serving of an ad, one possibledefinition of the conversion rate might only consider ads that have beenserved more than the predetermined time in the past.

A “property” is something on which ads can be presented. A property mayinclude online content (e.g., a Website, an MP3 audio program, onlinegames, etc.), offline content (e.g., a newspaper, a magazine, atheatrical production, a concert, a sports event, etc.), and/or offlineobjects (e.g., a billboard, a stadium score board, and outfield wall,the side of truck trailer, etc.). Properties with content (e.g.,magazines, newspapers, Websites, email messages, etc.) may be referredto as “media properties.” Although properties may themselves be offline,pertinent information about a property (e.g., attribute(s), topic(s),concept(s), category(ies), keyword(s), relevancy information, type(s) ofads supported, etc.) may be available online. For example, an outdoorjazz music festival may have entered the topics “music” and “jazz”, thelocation of the concerts, the time of the concerts, artists scheduled toappear at the festival, and types of available ad spots (e.g., spots ina printed program, spots on a stage, spots on seat backs, audioannouncements of sponsors, etc.).

A “document” is to be broadly interpreted to include anymachine-readable and machine-storable work product. A document may be afile, a combination of files, one or more files with embedded links toother files, etc. The files may be of any type, such as text, audio,image, video, etc. Parts of a document to be rendered to an end user canbe thought of as “content” of the document. A document may include“structured data” containing both content (words, pictures, etc.) andsome indication of the meaning of that content (for example, e-mailfields and associated data, HTML tags and associated data, etc.) Adspots in the document may be defined by embedded information orinstructions. In the context of the Internet, a common document is a Webpage. Web pages often include content and may include embeddedinformation (such as meta information, hyperlinks, etc.) and/or embeddedinstructions (such as JavaScript, etc.). In many cases, a document hasan addressable storage location and can therefore be uniquely identifiedby this addressable location. A universal resource locator (URL) is anaddress used to access information on the Internet.

A “Web document” includes any document published on the Web. Examples ofWeb documents include, for example, a Website or a Web page.

“Document information” may include any information included in thedocument, information derivable from information included in thedocument (referred to as “document derived information”), and/orinformation related to the document (referred to as “document relatedinformation”), as well as an extensions of such information (e.g.,information derived from related information). An example of documentderived information is a classification based on textual content of adocument. Examples of document related information include documentinformation from other documents with links to the instant document, aswell as document information from other documents to which the instantdocument links.

Content from a document may be rendered on a “content renderingapplication or device”. Examples of content rendering applicationsinclude an Internet browser (e.g., Explorer, Netscape, Opera, Firefox,etc.), a media player (e.g., an MP3 player, a Realnetworks streamingaudio file player, etc.), a viewer (e.g., an Abobe Acrobat pdf reader),etc.

A “content owner” is a person or entity that has some property right inthe content of a media property (e.g., document). A content owner may bean author of the content. In addition, or alternatively, a content ownermay have rights to reproduce the content, rights to prepare derivativeworks of the content, rights to display or perform the content publicly,and/or other proscribed rights in the content. Although a content servermight be a content owner in the content of the documents it serves, thisis not necessary. A “Web publisher” is an example of a content owner.

“Verticals” are groups of related products, services, industries,content formats, audience demographics, and/or topics that are likely tobe found in, or for, Website content.

A “cluster” is a group of elements that tend to occur closely together.For example, a cluster may be a set of terms that tend to co-occur often(e.g., on Web pages, in search queries, in product catalogs, in articles(online or offline) in speech, in discussion, in e-mail threads, etc.).

“User information” may include user behavior information and/or userprofile information.

“E-mail information” may include any information included in an e-mail(also referred to as “internal e-mail information”), informationderivable from information included in the e-mail and/or informationrelated to the e-mail, as well as extensions of such information (e.g.,information derived from related information). An example of informationderived from e-mail information is information extracted or otherwisederived from search results returned in response to a search querycomposed of terms extracted from an e-mail subject line. Examples ofinformation related to e-mail information include e-mail informationabout one or more other e-mails sent by the same sender of a givene-mail, or user information about an e-mail recipient. Informationderived from or related to e-mail information may be referred to as“external e-mail information.”

A “keyword” may be a word, phrase, or a portion of a word that conveysmeaning (e.g., a root).

§ 4.2 Exemplary Advertising Environments In Which, or With Which, thePresent Invention May Operate

FIG. 1 is a high-level diagram of an advertising environment. Theenvironment may include an ad entry, maintenance and delivery system(simply referred to as an ad server) 120. Advertisers 110 may directly,or indirectly, enter, maintain, and track ad information in the system120. The ads may be in the form of graphical ads such as so-calledbanner ads, text only ads, image ads, audio ads, video ads, adscombining one of more of any of such components, etc. The ads may alsoinclude embedded information, such as a link, and/or machine executableinstructions. Ad consumers 130 may submit requests for ads to, acceptads responsive to their request from, and provide usage information to,the system 120. An entity other than an ad consumer 130 may initiate arequest for ads. Although not shown, other entities may provide usageinformation (e.g., whether or not a conversion or selection related tothe ad occurred) to the system 120. This usage information may includemeasured or observed user behavior related to ads that have been served.

The ad server 120 may be similar to the one described in the '900application. An advertising program may include information concerningaccounts, campaigns, creatives, targeting, etc. The term “account”relates to information for a given advertiser (e.g., a unique e-mailaddress, a password, billing information, etc.). A “campaign” or “adcampaign” refers to one or more groups of one or more advertisements,and may include a start date, an end date, budget information,geo-targeting information, syndication information, etc. For example,Honda may have one advertising campaign for its automotive line, and aseparate advertising campaign for its motorcycle line. The campaign forits automotive line may have one or more ad groups, each containing oneor more ads. Each ad group may include targeting information (e.g., aset of keywords, a set of one or more topics, etc.), and priceinformation (e.g., cost, average cost, or maximum cost (per impression,per selection, per conversion, etc.)). Therefore, a single cost, asingle maximum cost, and/or a single average cost may be associated withone or more keywords, and/or topics. As stated, each ad group may haveone or more ads or “creatives” (That is, ad content that is ultimatelyrendered to an end user.). Each ad may also include a link to a URL(e.g., a landing Web page, such as the home page of an advertiser, or aWeb page associated with a particular product or server). Naturally, thead information may include more or less information, and may beorganized in a number of different ways.

FIG. 2 illustrates an environment 200 in which the present invention maybe used. A user device (also referred to as a “client” or “clientdevice”) 250 may include a browser facility (such as the Explorerbrowser from Microsoft, the Opera Web Browser from Opera Software ofNorway, the Navigator browser from AOL/Time Warner, the Firefox browserfrom Mozilla, etc.), an e-mail facility (e.g., Outlook from Microsoft),etc. A search engine 220 may permit user devices 250 to searchcollections of documents (e.g., Web pages). A content server 210 maypermit user devices 250 to access documents. An e-mail server (such asGMail from Google, Hotmail from Microsoft Network, Yahoo Mail, etc.) 240may be used to provide e-mail functionality to user devices 250. An adserver 210 may be used to serve ads to user devices 250. The ads may beserved in association with search results provided by the search engine220. However, content-relevant ads may be served in association withcontent provided by the content server 230, and/or e-mail supported bythe e-mail server 240 and/or user device e-mail facilities.

As discussed in the '900 application, ads may be targeted to documentsserved by content servers. Thus, one example of an ad consumer 130 is ageneral content server 230 that receives requests for documents (e.g.,articles, discussion threads, music, video, graphics, search results,Web page listings, etc.), and retrieves the requested document inresponse to, or otherwise services, the request. The content server maysubmit a request for ads to the ad server 120/210. Such an ad requestmay include a number of ads desired. The ad request may also includedocument request information. This information may include the documentitself (e.g., page), a category or topic corresponding to the content ofthe document or the document request (e.g., arts, business, computers,arts-movies, arts-music, etc.), part or all of the document request,content age, content type (e.g., text, graphics, video, audio, mixedmedia, etc.), geo-location information, document information, etc.

The content server 230 may combine the requested document with one ormore of the advertisements provided by the ad server 120/210. Thiscombined information including the document content and advertisement(s)is then forwarded towards the end user device 250 that requested thedocument, for presentation to the user. Finally, the content server 230may transmit information about the ads and how, when, and/or where theads are to be rendered (e.g., position, selection or not, impressiontime, impression date, size, conversion or not, etc.) back to the adserver 120/210. Alternatively, or in addition, such information may beprovided back to the ad server 120/210 by some other means.

Another example of an ad consumer 130 is the search engine 220. A searchengine 220 may receive queries for search results. In response, thesearch engine may retrieve relevant search results (e.g., from an indexof Web pages). An exemplary search engine is described in the article S.Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual SearchEngine,” Seventh International World Wide Web Conference, Brisbane,Australia and in U.S. Pat. No. 6,285,999 (both incorporated herein byreference). Such search results may include, for example, lists of Webpage titles, snippets of text extracted from those Web pages, andhypertext links to those Web pages, and may be grouped into apredetermined number of (e.g., ten) search results.

The search engine 220 may submit a request for ads to the ad server120/210. The request may include a number of ads desired. This numbermay depend on the search results, the amount of screen or page spaceoccupied by the search results, the size and shape of the ads, etc. Inone embodiment, the number of desired ads will be from one to ten, andpreferably from three to five. The request for ads may also include thequery (as entered or parsed), information based on the query (such asgeolocation information, whether the query came from an affiliate and anidentifier of such an affiliate), and/or information associated with, orbased on, the search results. Such information may include, for example,identifiers related to the search results (e.g., document identifiers or“docIDs”), scores related to the search results (e.g., informationretrieval (“IR”) scores such as dot products of feature vectorscorresponding to a query and a document, Page Rank scores, and/orcombinations of IR scores and Page Rank scores), snippets of textextracted from identified documents (e.g., Web pages), full text ofidentified documents, topics of identified documents, feature vectors ofidentified documents, etc.

The search engine 220 may combine the search results with one or more ofthe advertisements provided by the ad server 120/210. This combinedinformation including the search results and advertisement(s) is thenforwarded towards the user that submitted the search, for presentationto the user. Preferably, the search results are maintained as distinctfrom the ads, so as not to confuse the user between paid advertisementsand presumably neutral search results.

Finally, the search engine 220 may transmit information about the ad andwhen, where, and/or how the ad was to be rendered (e.g., position,selection or not, impression time, impression date, size, conversion ornot, etc.) back to the ad server 120/210. Alternatively, or in addition,such information may be provided back to the ad server 120/210 by someother means.

Finally, the e-mail server 240 may be thought of, generally, as acontent server in which a document served is simply an e-mail. Further,e-mail applications (such as Microsoft Outlook for example) may be usedto send and/or receive e-mail. Therefore, an e-mail server 240 orapplication may be thought of as an ad consumer 130. Thus, e-mails maybe thought of as documents, and targeted ads may be served inassociation with such documents. For example, one or more ads may beserved in, under over, or otherwise in association with an e-mail.

Although the foregoing examples described servers as (i) requesting ads,and (ii) combining them with content, one or both of these operationsmay be performed by a client device (such as an end user computer forexample).

§ 4.3 EXEMPLARY EMBODIMENTS

As described below, in at least some embodiments consistent inventionthe present invention, given keyword(s) and/or document information(e.g., Website information) as input, such embodiments may return one ormore relevant verticals and/or information of one or more relevantdocuments (e.g., information of relevant Websites belonging to an adnetwork) as output.

Thus, at least some embodiments consistent with the present inventionmay output Websites for input keywords. That is, for example, given alist of keywords, a list of Websites in an ad network that representsthe verticals suggested by these keywords may be returned. Example:input query=food->output reply=www.hungrymonster.com, foodgeeks.com,homecooking.about.com, . . . .

At least some embodiments consistent with the present invention mayoutput verticals categories for input keywords. Thus, for example, givena list of keywords, a list of vertical categories may be returned.Example: input query=anime->output reply=/Entertainment/Entertainment(Other)/Comics & Animation/Anime & Manga.

At least some embodiments consistent with the present invention mayoutput Websites for input other Websites. Thus, for example, given alist of Websites, a list of Websites in an ad network, that have thesame or related vertical categories, may be returned. Example: inputquery=www.tomshardware.com->output reply=www.anandtech.com,www.hardocp.com, www.overclockers.com, . . . .

FIG. 3 is a bubble diagram of exemplary operations that may be performedin a manner consistent with the present invention, as well asinformation that may be used and/or generated by such operations.Suggestion operations 320 may accept a keyword 305 and/or documentinformation (e.g., a URL of a Website) and output relevant (e.g.,vertical) categories and/or documents (e.g., Website).

The suggestion operations 320 may use one or more of document-clusterassociations 322, keyword-cluster associations 324, cluster-documentassociations 326 and cluster-category associations 328 to determinerelevant categories and/or documents given a keyword and/or a document.The clusters may be semantic clusters such as term co-occurrenceclusters for example. For example, if a keyword is input,keyword-cluster associations 324 may be used to determine one or moreclusters. At least some of the determined cluster(s) andcluster-document associations 326 may be used to determine one or moredocuments. Similarly, at least some of the determined cluster(s) andcluster-category associations 328 may be used to determine one or morecategories. As another example, if document information is input,document-cluster associations 322 may be used to determine one or moreclusters. At least some of the determined cluster(s) andcluster-document associations 326 may be used to determine one or moredocuments. Similarly, at least some of the determined cluster(s) andcluster-category associations 328 may be used to determine one or morecategories. Suggestion operations 320 may perform data reduction and/orfiltering operations to reduce/filter clusters, documents, and/orcategories. If the suggestion operations 320 used categories, they 320may be thought of as category-based suggestion operations 320.

As shown, the determined relevant categories 330 may include “trafficestimates” (e.g., number of pageviews over a given time period, numberof readers, number of expected ad impressions over a given time period,etc.). As also shown, the resulting documents 330 may be scored and/orsorted by document sorting/scoring operations 340. Such documents mayalso be filtered by document filtering operations 350. Such operations340 and/or 350 may be used to provide only the most relevant documentsas output. Similarly, operations (not shown) may be used to score, sort,and/or filter relevant categories.

Still referring to FIG. 3, advertiser feedback operations 360 may beused to accept user input from advertisers. For example, in the contextof targeting ads, an advertiser may select categories and/or documentswith which they wish to serve their ads. They may also provide offerinformation (e.g., offer per impression, offer per selection, offer perconversion, maximum offer per impression, maximum offer per selection,maximum offer per conversion, etc.) in association with document(s)and/or categories. Thus, for example, after being provided with thevertical category “/Computers & Technology/Consumer Electronics/AudioEquipment/MP3 Players”, the advertiser may wish to offer $0.50 perimpression to have its ad shown on Websites belonging to this verticalcategory. As another example, after being provided with the top 50Websites in the vertical category “/Automotive/Auto Parts/VehicleTires”, the advertiser may browse the Websites (e.g., using linksprovided as a part of the output 330) select 12 of those Websites andprovide an offer of $0.75 per impression to have its ad shown on any ofthe 12 selected Websites, and an offer of $5.00 per selection to haveits ad shown on any of the 50 Websites provided.

Note that document selection/de-selection 362 by an advertiser may beused to adjust document-cluster associations 322 (if documentinformation was input 310), keyword-cluster associations 324 (if keywordinformation was input 305), and cluster-document associations 326.Similarly, category selection/de-selection 362 by an advertiser may beused to adjust document-cluster associations 322 (if documentinformation was input 310), keyword-cluster associations 324 (if keywordinformation was input 305), and cluster-category associations 328.

Referring back to associations 322, 324, 326 and 328, such associationsmay be indexes. Such indexes may be created and/or maintained usingtechniques described in U.S. patent application Ser. No. 11/______(referred to as “the '______ application” and incorporated herein byreference), titled “CATEGORIZING OBJECTS, SUCH AS DOCUMENTS AND/ORCLUSTERS, WITH RESPECT TO A TAXONOMY AND DATA STRUCTURES DERIVED FROMSUCH CATEGORIZATION”, filed on Apr. 22, 2005, and listing DavidGehrking, Ching Law, and Andrew Maxwell as inventors. Further, any ofthe other indexes or data associations described in the '______application may be used in the context of the present invention.

§ 4.3.1 EXEMPLARY METHODS

FIG. 4 is a flow diagram of an exemplary method 400 for determiningcategory and/or document suggestions from one or more input keywords, ina manner consistent with the present invention. One or more keywords areaccepted (Block 410) and a set of one or more clusters is determinedusing the keyword(s) (Block 420). The clusters may be scored, sorted,and/or filtered (e.g., based on an ordering, a threshold score, etc.).(Block 430). A set of one or more taxonomy categories (e.g., verticals)may then be determined using the cluster(s). (Block 440) The categoriesmay be scored, sorted, and/or filtered (e.g., based on an ordering, athreshold score, etc.). (Block 450) The one or more (e.g., remaining)taxonomy categories may then be returned. (Block 460). If the output isto simply include one or more categories, the method 400 may be left atthis point. If, however, the output is to include one or more documents(e.g., instead of, or in addition to, categories), the method 400 mayfurther determine a set of one or more documents using the taxonomycategories 470. The documents may be scored, sorted, and/or filtered.(Block 480) Finally, one or more (e.g., remaining) documents may then bereturned. (Block 490) Various acts of the method 400 may be performed asdescribed in the '______ application introduced above.

FIG. 5 is a flow diagram of an exemplary method 500 for determiningcategory and/or document suggestions from one or more input documents,in a manner consistent with the present invention. Information (e.g.,identifiers) of one or more documents is accepted (Block 505) and themethod 500 may perform one or more of the acts that follow.

Referring first to the left branch of the method 500, a set of one ormore taxonomy categories (e.g., verticals) is determined using thedocument information. (Block 510) The taxonomy categories may be scored,sorted, and/or filtered (Block 515) and the (e.g., remaining) taxonomycategories may be returned. (Block 520) A set of one or more documents(e.g., Websites) may be determined using at least some of the (e.g.,remaining) taxonomy categories. (Block 525) These documents may bescored, sorted, and/or filtered (Block 530) and the (e.g., remaining)documents may be returned (Block 535) before the method 500 is left(Node 560).

Now referring to the right branch of the method 500, a set of one ormore clusters may be determined using the document information. (Block540) The clusters may be scored, sorted, and/or filtered. (Block 545). Aset of one or more taxonomy categories may be determined using the(e.g., remaining) clusters (Block 550), these taxonomy categories may beused with those of block 510, and the method 500 may continue at Block515. A set of one or more documents may be determined using the clusters(Block 555), these documents may be used with those of block 525, andthe method may continue at Block 530.

Referring back to the methods 400 and 500 of FIGS. 4 and 5, the documentinformation may be document identifiers. Thus, for example, if thedocuments are Web pages, the document information may be URLs, and ifthe documents are Websites, the document information may be URLs of thehome pages of the Websites.

Still referring to FIGS. 4 and 5, the clusters may be semantic clusters,such as term co-occurrence clusters. An example of operations used togenerate and/or identify such clusters is a probabilistic hierarchicalinferential learner (referred to as “PHIL”), such as described in U.S.Provisional Application Ser. No. 60/416,144 (referred to as “the '144provisional” and incorporated herein by reference), titled “Methods andApparatus for Probabilistic Hierarchical Inferential Learner,” filed onOct. 3, 2002, and U.S. patent application Ser. No. 10/676,571 (referredto as “the '571 application” and incorporated herein by reference),titled “Methods and Apparatus for Characterizing Documents Based onCluster Related Words,” filed on Sep. 30, 2003 and listing Georges Harikand Noam Shazeer as inventors.

Still referring to FIGS. 4 and 5, filtering may be performed based on anordering and/or based on a threshold score. Thus, for example, for anordered set of results, filtering may be used to take only the top Nresults. As another example, for a scored set of results, filtering maybe used to take only those results that exceed a threshold value. Thethreshold value may be dynamically determined or predetermine. Indeed,multiple thresholds may be used.

FIG. 6 is a flow diagram of an exemplary method 600 for providing anadvertiser user interface, in a manner consistent with the presentinvention. As indicated by event block 605, various branches of themethod 600 may be performed in response to the occurrence of variousevents. For example, if a set of one or more documents is returned(Recall, e.g., 490 and 535 of FIGS. 4 and 5, respectively.), informationabout such documents is presented to the user and the method 600branches back to event block 605. (Block 610) If a set of one or moretaxonomy categories is returned (Recall, e.g., 460 and 520 of FIGS. 4and 5, respectively.), the taxonomy categories are presented to the userand the method 600 branches back to event block 605. (Block 615) Ifdocument information is input by the user (Recall, e.g., 505 of FIG.5.), then the document information is provided to the suggestionoperations as input and the method 600 branches back to event block 605.(Block 620) If one or more keywords are input by the user (Recall, e.g.,410 of FIG. 4.), then the keyword(s) is provided to the suggestionoperations as input and the method 600 branches back to event block 605.(Block 625) If a filter request is input by the user, documents and/ortaxonomy categories may be filtered and the method 600 branches back toevent block 605. (Block 630) If a selection is input by the user, theselection is saved (Block 640), ad campaign management routines may becalled (Block 645) and the method 600 branches back to event block 605.If a request to check a document is input by the user, the selecteddocument is rendered to the user and the method 600 branches back toevent block 605. (Block 650) If a document and/or category is deselectedby the user, the selection is removed (Block 660), the deselection maybe flagged for analysis (Block 665) and the method 600 branches back toevent block 605. If the user requests a session summary, a sessionsummary is provided to the user and the method 600 branches back toevent block 605. (Block 670) The method 600 may be left if the userprovides an exit command. (Node 680)

Referring back to block 665, in at least one embodiment consistent withthe present invention, Websites deselected from suggestion lists may beidentified (e.g., flagged) for human evaluation, for example to see ifthey belong in a different category, or should be removed from the adnetwork.

§ 4.3.2 EXEMPLARY APPARATUS

FIG. 7 is high-level block diagram of a machine 700 that may perform oneor more of the operations discussed above. The machine 700 basicallyincludes one or more processors 710, one or more input/output interfaceunits 730, one or more storage devices 720, and one or more system busesand/or networks 740 for facilitating the communication of informationamong the coupled elements. One or more input devices 732 and one ormore output devices 734 may be coupled with the one or more input/outputinterfaces 730.

The one or more processors 710 may execute machine-executableinstructions (e.g., C or C++ running on the Solaris operating systemavailable from Sun Microsystems Inc. of Palo Alto, Calif. or the Linuxoperating system widely available from a number of vendors such as RedHat, Inc. of Durham, N.C.) to perform one or more aspects of the presentinvention. At least a portion of the machine executable instructions maybe stored (temporarily or more permanently) on the one or more storagedevices 720 and/or may be received from an external source via one ormore input interface units 730.

In one embodiment, the machine 700 may be one or more conventionalpersonal computers. In this case, the processing units 710 may be one ormore microprocessors. The bus 740 may include a system bus. The storagedevices 720 may include system memory, such as read only memory (ROM)and/or random access memory (RAM). The storage devices 720 may alsoinclude a hard disk drive for reading from and writing to a hard disk, amagnetic disk drive for reading from or writing to a (e.g., removable)magnetic disk, and an optical disk drive for reading from or writing toa removable (magneto-) optical disk such as a compact disk or other(magneto-) optical media.

A user may enter commands and information into the personal computerthrough input devices 732, such as a keyboard and pointing device (e.g.,a mouse) for example. Other input devices such as a microphone, ajoystick, a game pad, a satellite dish, a scanner, or the like, may also(or alternatively) be included. These and other input devices are oftenconnected to the processing unit(s) 710 through an appropriate interface730 coupled to the system bus 740. The output devices 734 may include amonitor or other type of display device, which may also be connected tothe system bus 740 via an appropriate interface. In addition to (orinstead of) the monitor, the personal computer may include other(peripheral) output devices (not shown), such as speakers and printersfor example.

Referring back to FIG. 2, one or more machines 700 may be used as enduser client devices 250, content servers 230, search engines 220, emailservers 240, and/or ad servers 210.

§ 4.3.3 Refinements and Alternatives

Although many of the exemplary embodiments are described in the contextof online documents such as Websites, embodiments consistent with thepresent invention may be used in the context of offline media propertiessuch as newspapers, periodicals, theatrical performances, concerts,sports events, etc. However, information about such offline mediaproperties should be available in machine readable form.

At least some embodiments consistent with the present invention mayallow advertisers to filter Website outputs, for example, so that thenumber of Websites returned is limited, so that the languages of thereturned Websites are restricted, etc.

In at least some embodiments consistent with the present invention, fora single keyword query, the results of Keywords->Verticals andKeywords->Websites may be combined to generate a general set of Websitesof all senses of the keyword, plus the top Websites associated with theverticals suggested by the keyword. Advertising users may then refinetheir general Website lists by sense (vertical).

In at least some embodiments consistent with the present invention, theadvertiser user may to enter Websites not in the ad network to findsimilar Websites that are in the ad network. In such embodiments,document-cluster associations (Recall, e.g., 322 of FIG. 3.) will not belimited to documents (Websites) in the ad network. Websites outside ofthe advertising network may be crawled on demand, or pre-crawled andindexed (particularly if demand is high).

At least some embodiments consistent with the present invention maypermit the Websites to be sorted for review by various attributes (e.g.relevancy to the keywords or Websites entered by the advertiser, Websitepageviews, CPM price of the Website, etc.).

At least some embodiments consistent with the present invention maygroup suggested Websites in order to allow the advertiser user to easilyset an offer (e.g., a per impression bid) across a large number ofWebsites. For example, Website suggestions may be grouped by relevancyto the keywords or Websites entered by the advertiser, Websitepageviews, CPM price of the Website, etc. At least some embodimentsconsistent with the present invention may estimate ad impressions (orselections, or conversions) across such a group of Websites given CPM(price per impression) or CPC (price per click) offer information.

In at least some embodiments consistent with the present inventionWebsites deselected from Website suggestion lists may be tagged forhuman evaluation to help improve the Website selection and/or scoring(e.g., relevancy) algorithm. Alternatively, or in addition, humanevaluation may be used to determine if the Websites should be removedfrom the ad network (e.g., due to quality issues).

In at least some embodiments consistent with the present invention, if aWebsite selected, but is not active (e.g., because it is not in the adnetwork, because the Website publisher has not given permission topublicly name its Website as part of the ad network, etc.), theadvertiser's ads may automatically become eligible for serving with theWebsite if and when the Website becomes part of the ad network.

In at least some embodiments consistent with the present invention,Website owners (or owners of some other properties) may provideadditional data such as Website description, audience demographics,and/or other structured or unstructured data. In at least someembodiment consistent with the present invention, advertiser users mayuse such additional data for searching and/or sorting results.

§ 4.4 EXAMPLES OF OPERATIONS Example 1

FIGS. 8-11 illustrate exemplary user interfaces, consistent with anexemplary embodiment consistent with the present invention, whichillustrate an exemplary use of the embodiment. Suppose anadvertiser—“Blue Ridge Beverages”—wants to place one of its ads oncertain Websites. In the past, the advertiser might have to either (a)negotiate placing its ad on various Websites concerning wine, or (b)have its ad run on an ad network, likely in an overly-broad category(e.g., food and beverages).

FIG. 8 illustrates a display screen 800 including a portion of aWebpage, consistent with the present invention, for helping theadvertiser target the serving of its ad on relevant Webpages of an adnetwork. The user may have selected “Target ad” hypertext 810 to obtainthe display screen 800. (Further hypertext to, for example, “Setpricing” 820, “Set daily budget” 830 and “Review and save” 840 may alsobe provided.) Section 850 of the display screen 800 is used to help theadvertiser identify Websites on which it may wish to target its ads. Theadvertiser may provide keywords and/or Websites that it believes arerelevant in boxes 860 and 870, respectively.

As an example, the advertiser may already participate in ad search querykeyword relevant advertising (e.g., AdWords from Google) and may usecertain keywords (e.g., Wine, Wine tasting, Wine enthusiast, andCalifornia wine) in that campaign. Naturally, the source of the keywordsneed not be a preexisting search query keyword relevant ad campaign. Asanother example, the advertiser may know that it wants advertise oncertain Websites (e.g., www.winesite1.com, and winesite2.com) of whichit is already aware. FIG. 9 illustrates a portion 900 of a displayscreen having a section 850′ in which blocks 860′ and 870′ includeadvertiser entered keywords and Websites, respectively. The advertisermay then request relevant Websites belonging to the ad network byselecting the “Find Sites>” button 910.

FIG. 10 illustrates a display screen 1000 including a portion of aWebpage including results of the “Find Sites” request, given the inputkeywords and Websites shown in blocks 860′ and 870′ of FIG. 9. Theresults 1010 include a number of entries. The advertiser may filter theresulting Websites. For example, drop down menu 1015 may allow theadvertiser to show only those Websites that accept text and image ads,Websites that accept image ads, Websites that accept text ads, etc. Eachof the entries may include a Website address 1030 with a link to theWebsite. In this way, an advertiser can view the Website by selectingthe hypertext link 1030. Each entry may also include statistics for theWebsite, such as the number of impressions (pageviews) per day 1040 forexample. The advertiser may add or remove Websites to a set of one ormore Websites, shown in box 1050, on which the advertiser wishes to showits ad. For example, the advertiser may check boxes 1025 and may usebuttons 1055 and 1060 to add and remove, respectively, such Websites. Abutton 1020 may be provided to allow all entries to be selected(checked) by the advertiser. Finally, as shown, a button 1070 may beprovided to allow the advertiser to use the Websites in the box 1050 asinput (just as the Websites listed in the box 870′ were used) to findother Websites (e.g., Websites categorized in the same or similarvertical categories as the input Website(s).).

Suppose that the advertiser has added a number of Websites to a set ofWebsites that it wants to serve its ad with. A portion of the Webpage(not shown) displayed on screen 1000 may include a command element(e.g., like 820, 830, 840 of FIG. 8.) to allow the advertiser to providead campaign information used to target the serving of its ad on variousones of the selected Websites. Referring to FIG. 11 for example, ascreen portion 1100 may include information about the ad creative, suchas a thumbnail image of the ad 1110, as well as a number of entries1120. Each entry may include a check box 1130, a text (perhaps with alink) of the Website 1140, status information about whether or not theWebsite participates presently in the ad network 1150, offer informationentered by the advertiser 1160, and various statistics of the ad 1170such as selections (clicks), impressions, selection rates (CTRs),average cost per thousand impressions (CPM), total cost, etc. Date rangeinformation for the ad campaign may also be provided by the advertiseras indicated by tool elements 1180.

Although the foregoing example illustrates how embodiments consistentwith the present invention may be used to suggest Websites to betargeted by an advertiser, the present invention is not limited to suchembodiments. For example, as discussed above, embodiments consistentwith the present invention may be used to suggest vertical categories tobe targeted by the advertiser.

Example 2

Suppose BMW wants to set up a brand-building ad campaign within an adnetwork. For example, suppose it has a “BMW—as refined as fine wine” adcampaign in which they want to target to wine drinkers (who are highlycorrelated with luxury car buyers). BMW may use a Website suggestiontool consistent with the present invention to enter wine.com andwinespectator.com (Recall, e.g., 870 of FIG. 8.) as two examples ofWebsites its wants to target. The Website suggestion tool looks up bothentered Websites and finds the most popular clusters (e.g., philclusters) for each. (Recall, e.g., 322 of FIG. 3.)

Using the clusters, the Website suggestion tool can use cluster-documentassociations (Recall, e.g., 326 of FIG. 3.), and/or cluster-categoryassociations (Recall, e.g., 328 of FIG. 3.) and category-documentassociations to return the top N (e.g., N=500) Websites sorted by arelevancy score.

Using a filtering tool, BMW can focus on the vertical categories and/orWebsites that it believes are the most relevant. Interesting statisticssuch as pageviews, min CPM (e.g., as specified by Web publishers) andaverage CPM (e.g., of offers by other advertisers for the Website) forthe Website may be provided to the advertiser. BMW may use filtering andcheckbox selection to pick the Websites for which to bid a particularCPM, and applies that CPM to the selected Websites. These settings canlater be tweaked using the same mechanism. Suppose BMW enters a “maxnumber of times a user can view ad” frequency cap of 3 to get a dailypageviews estimate of 200 K and a daily spend estimate of $1,000.00.

Suppose that as BMW scans the list of Websites, a couple of the Websiteslook questionable and after clicking on them and reviewing the contentof the Website, it deselects these Websites from the list. Thesedeselections may be flagged for (e.g., manual) quality review.

Suppose BMW has a large enough budget to expand the list further, sothey click an “add more sites” button and enter “fine cuisine” as akeyword. (Recall, e.g., box 860 of FIG. 8.) Suppose another 100 Websitesare returned, most of which are only loosely relevant. Nonetheless,suppose that BMW still finds 15 Websites which they select (e.g., theycan deselect all and select just these 15) and sets a CPM bid of $3 forthis set of Websites.

Finally, suppose that BMW leaves an “automatically notify me of newsites similar to my target list” selection tool element checked.Consequently, suppose that two weeks later that BMW is notified that newWebsites have been added to the ad network that are considered relevant,with an invitation to add these Websites to BMW's set of targetedWebsites.

Suppose that a final summary (Recall, e.g., hypertext 840 of FIG. 8.)gives a daily page view estimate of 300 K and daily spend estimate of$1,250.00 which meets BMWs target spend.

Example 3

Suppose that Google wants to advertise for software developers and setsup a “Google developers wanted” text ad in AdWords. Suppose further thatGoogle enters “Slashdot.com” and “freshmeat.com” into the Websitesuggestion tool. (Recall, e.g., box 870 of FIG. 8.) A list of developercommunity Websites including Slashdot are presented to the advertiser asoutput. Suppose that the Website “Freshmeat” is not in the ad network,so it is shown as “inactive”. Although Google may have initially onlywanted to advertise on Slashdot, it may change its mind after beingpresented with 10 very similar “developer community websites.”Consequently, it may decide to bid $5.00 CPM on all of them. SupposeGoogle doesn't want to be notified of new Websites, and thereforeunchecks the “automatically notify me of new sites similar to my targetlist” checkbox. Suppose further that Google uses a default “max numberof times a user can view ad” value of 5, since presenting users with thead more than this may be perceived to be “spammy.”

Suppose that later, the Website “Freshmeat” joins the ad network. Inthis case, the $5.00 CPM bid on the “Freshmeat.com” Website mayautomatically become active.

§ 4.5 CONCLUSIONS

As can be appreciated from the foregoing, embodiments consistent withthe present invention can be used to help advertisers to better targettheir advertising campaign by providing relevant media properties (e.g.,Websites or Webpages), and/or relevant vertical categories in responseto keywords and/or Websites provided by the advertiser. More granularverticals, customized to advertiser input (e.g., keywords, demographics,etc.), can be supported. For example, an advertiser couldchoose/Computers & Technology and then narrow it by searching on thekeyword “Mac”.

1. A computer-implemented method comprising: a) accepting one or morekeywords; and b) determining a set of one or more taxonomy categoriesusing at least some of the one or more keywords.
 2. Thecomputer-implemented method of claim 1 wherein each of the taxonomycategories is a vertical category, the method further comprising: c)presenting at least one of the set of one or more determined taxonomycategories to an advertising user as an ad targeting suggestion.
 3. Thecomputer-implemented method of claim 2 wherein each of the taxonomycategories has at least one property associated with it, and wherein theat least one property participates in an advertising network.
 4. Thecomputer-implemented method of claim 2 wherein each of the taxonomycategories has at least one Web document associated with it, and whereinthe at least one Web document participates in an advertising network. 5.The computer-implemented method of claim 4 further comprising: d)accepting an advertiser selection of a suggested taxonomy category; ande) targeting the serving of an ad of the advertiser to each of the atleast one Web document associated with the selected suggested taxonomycategory.
 6. The computer-implemented method of claim 5 furthercomprising: f) accepting from the advertiser, an offer for associationwith the selected suggested taxonomy category.
 7. Thecomputer-implemented method of claim 6 wherein the offer is selectedfrom a group of offers consisting of (A) an offer per impression, (B) amaximum offer per impression, (C) an offer per selection, (D) a maximumoffer per selection, (E) an offer per conversion, and (F) a maximumoffer per conversion.
 8. The computer-implemented method of claim 1further comprising: c) determining a set of one or more properties usingat least some of the determined one or more taxonomy categories.
 9. Thecomputer-implemented method of claim 8 wherein each of the one or moreproperties is a Web document.
 10. The computer-implemented method ofclaim 8 wherein the property is a document, the method furthercomprising: d) presenting at least one of the set of one or moredetermined documents to an advertising user as an ad targetingsuggestion.
 11. The computer-implemented method of claim 10 furthercomprising: e) presenting to the advertising user, in association witheach of the one or more determined documents presented to theadvertising user, viewing information of the document.
 12. Thecomputer-implemented method of claim 11 wherein the viewing informationof the document is a number of pageviews for the document over a giventime period.
 13. The computer-implemented method of claim 10 furthercomprising: e) accepting an advertiser selection of a suggesteddocument; and f) targeting the serving of an ad of the advertiser withthe selected suggested document.
 14. The computer-implemented method ofclaim 13 further comprising: g) accepting from the advertiser, an offerfor association with the selected suggested document.
 15. Thecomputer-implemented method of claim 14 wherein the offer is selectedfrom a group of offers consisting of (A) an offer per impression, (B) amaximum offer per impression, (C) an offer per selection, (D) a maximumoffer per selection, (E) an offer per conversion, and (F) a maximumoffer per conversion.
 16. The computer-implemented method of claim 14further comprising: h) determining a spend estimate using the offer andviewing information associated with the selected suggested document; andi) presenting the spend estimate to the advertiser user.
 17. Thecomputer-implemented method of claim 1 wherein the act of determining aset of one or more taxonomy categories using at least some of the one ormore keywords includes determining a set of one or more semanticclusters using the accepted one or more keywords, and determining a setof one or more taxonomy categories using at least some of the one ormore semantic clusters.
 18. The method of claim 17 wherein the semanticclusters are term co-occurrence clusters.
 19. The method of claim 17wherein the semantic clusters include at least one of (A) keywords thattend to co-occur in Web documents, (B) keywords that tend to co-occur inindividual search queries, and (C) keywords that tend to co-occur insearch sessions.
 20. A computer-implemented method comprising: a)accepting information about one or more properties; and b) determining aset of one or more taxonomy categories using at least some of theinformation of the one or more properties.
 21. The computer-implementedmethod of claim 20 wherein each of the taxonomy categories is a verticalcategory, the method further comprising: c) presenting at least one ofthe set of one or more determined taxonomy categories to an advertisinguser as an ad targeting suggestion.
 22. The computer-implemented methodof claim 21 wherein each of the taxonomy categories has at least onemedia property associated with it, and wherein the at least one propertyparticipates in an advertising network.
 23. The computer-implementedmethod of claim 21 wherein each of the taxonomy categories has at leastone Web document associated with it, and wherein the at least one Webdocument participates in an advertising network.
 24. Thecomputer-implemented method of claim 22 wherein the property is adocument, the method further comprising: d) accepting an advertiserselection of a suggested taxonomy category; and e) targeting the servingof an ad of the advertiser to each of the at least one documentassociated with the selected suggested taxonomy category.
 25. Thecomputer-implemented method of claim 24 further comprising: f) acceptingfrom the advertiser, an offer for association with the selectedsuggested taxonomy category.
 26. The computer-implemented method ofclaim 25 wherein the offer is selected from a group of offers consistingof (A) an offer per impression, (B) a maximum offer per impression, (C)an offer per selection, (D) a maximum offer per selection, (E) an offerper conversion, and (F) a maximum offer per conversion.
 27. Thecomputer-implemented method of claim 20 further comprising: c)determining a set of one or more additional media properties using atleast some of the determined one or more taxonomy categories.
 28. Thecomputer-implemented method of claim 20 further comprising: c)determining a set of one or more additional documents using at leastsome of the determined one or more taxonomy categories.
 29. Thecomputer-implemented method of claim 28 wherein each of the one or moreadditional documents is a Web document.
 30. The computer-implementedmethod of claim 28 further comprising: d) presenting at least one of theset of one or more determined additional documents to an advertisinguser as an ad targeting suggestion.
 31. The computer-implemented methodof claim 30 further comprising: e) presenting to the advertising user,in association with each of the one or more determined additionaldocuments presented to the advertising user, viewing information of theadditional document.
 32. The computer-implemented method of claim 31wherein the viewing information of the document is a number of pageviewsfor the additional document over a given time period.
 33. Thecomputer-implemented method of claim 30 further comprising: e) acceptingan advertiser selection of a suggested additional document; and f)targeting the serving of an ad of the advertiser with the selectedsuggested additional document.
 34. The computer-implemented method ofclaim 33 further comprising: g) accepting from the advertiser, an offerfor association with the selected suggested additional document.
 35. Thecomputer-implemented method of claim 34 wherein the offer is selectedfrom a group of offers consisting of (A) an offer per impression, (B) amaximum offer per impression, (C) an offer per selection, (D) a maximumoffer per selection, (E) an offer per conversion, and (F) a maximumoffer per conversion.
 36. The computer-implemented method of claim 34further comprising: h) determining a spend estimate using the offer andviewing information associated with the selected suggested additionaldocument; and i) presenting the spend estimate to the advertiser user.37. The computer-implemented method of claim 20 wherein the act ofdetermining a set of one or more taxonomy categories using at least someof the one or more media properties includes determining a set of one ormore semantic clusters using the accepted one or more keywords, anddetermining a set of one or more taxonomy categories using at least someof the one or more semantic clusters.
 38. The method of claim 37 whereinthe semantic clusters are term co-occurrence clusters.
 39. The method ofclaim 38 wherein the semantic clusters include at least one of (A)keywords that tend to co-occur in Web documents, (B) keywords that tendto co-occur in individual search queries, and (C) keywords that tend toco-occur in search sessions.
 40. Apparatus comprising: a) means foraccepting one or more keywords; and b) means for determining a set ofone or more taxonomy categories using at least some of the one or morekeywords.
 41. Apparatus comprising: a) means for accepting informationabout one or more properties; and b) means for determining a set of oneor more taxonomy categories using at least some of the information ofthe one or more properties.